Distribution shifts between training and deployment data often affect the
performance of machine learning models. In this paper, we explore a setting
where a hidden variable induces a shift in the distribution of classes. These
distribution shifts are particularly challenging for zero-shot classifiers, as
they rely on representations learned from training classes, but are deployed on
new, unseen ones. We introduce an algorithm to learn data representations that
are robust to such class distribution shifts in zero-shot verification tasks.
We show that our approach, which combines hierarchical data sampling with
out-of-distribution generalization techniques, improves generalization to
diverse class distributions in both simulations and real-world datasets.

在这篇研究论文中，我们研究了一个隐藏变量引起的类别分布偏移的情景，针对零样本分类器在训练课程上学习的表示被应用于新的未见类别的情况，我们引入了一种算法用于学习在这种类别分布偏移下具有鲁棒性的数据表示。我们展示了我们的方法，将分层数据抽样与越域泛化技术相结合，能够改进在模拟和真实数据集中对多样的类别分布的泛化能力。